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Advances in Fuzzy Systems
Volume 2012, Article ID 920920, 7 pages
Research Article

Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network

1Department of Computer Science, University of Manitoba, Winnipeg MB, Canada R3T 2N2
2Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, Canada T6R 2G7

Received 28 July 2011; Accepted 8 December 2011

Academic Editor: Maysam Abbod

Copyright © 2012 Nick J. Pizzi and Witold Pedrycz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. L. Pavia, G. M. Lampman, G. S. Kriz, and J. A. Vyvyan, Introduction to Spectroscopy, Harcourt Brace College, Fort Worth, Tex, USA, 2008.
  2. M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations, Cambridge University Press, Cambridge, UK, 2009.
  3. W. Pedrycz, D. J. Lee, and N. J. Pizzi, “Representation and classification of high-dimensional biomedical spectral data,” Pattern Analysis & Applications, vol. 13, no. 4, pp. 423–436, 2010. View at Google Scholar
  4. N. J. Pizzi and W. Pedrycz, “Aggregating multiple classification results using fuzzy integration and stochastic feature selection,” International Journal of Approximate Reasoning, vol. 51, no. 8, pp. 883–894, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Y. Kuo and I. H. Sloan, “Lifting the curse of dimensionality,” Notices of the American Mathematical Society, vol. 52, no. 11, pp. 1320–1328, 2005. View at Google Scholar · View at Scopus
  6. I. V. Oseledets and E. E. Tyrtyshnikov, “Breaking the curse of dimensionality, or how to use SVD in many dimensions,” SIAM Journal on Scientific Computing, vol. 31, no. 5, pp. 3744–3759, 2009. View at Publisher · View at Google Scholar
  7. N. J. Pizzi and W. Pedrycz, “A fuzzy logic network for pattern classification,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society, pp. 53–58, Cincinnati, Ohio, USA, June 2009,.
  8. N. Pizzi, L. P. Choo, J. Mansfield et al., “Neural network classification of infrared spectra of control and Alzheimer's diseased tissue,” Artificial Intelligence in Medicine, vol. 7, no. 1, pp. 67–79, 1995. View at Publisher · View at Google Scholar · View at Scopus
  9. U. M. Braga-Neto and E. R. Dougherty, “Is cross-validation valid for small-sample microarray classification?” Bioinformatics, vol. 20, no. 3, pp. 374–380, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Efron and G. Gong, “A leisurely look at the bootstrap, the jackknife, and cross-validation,” The American Scientist, vol. 37, no. 1, pp. 36–48, 1983. View at Google Scholar
  11. J. A. Swets, “Measuring the accuracy of diagnostic systems,” Science, vol. 240, no. 4857, pp. 1285–1293, 1988. View at Google Scholar · View at Scopus
  12. B. S. Everitt, “Moments of the statistics kappa and weighted kappa,” The British Journal of Mathematical and Statistical Psychology, vol. 21, pp. 97–103, 1968. View at Google Scholar
  13. G. A. F. Seber, Multivariate Observations, John Wiley & Sons, Hoboken, NJ, USA, 2004.
  14. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, UK, 2002.
  15. V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  16. L. Wang, Support Vector Machines: Theory and Applications, Springer, Berlin, Germany, 2005.
  17. V. Vapnik and A. Lerner, “Pattern recognition using generalized portrait method,” Automation and Remote Control, vol. 24, pp. 774–780, 1963. View at Google Scholar
  18. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, Cambridge, UK, 3rd edition, 2007.
  19. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. N. K. Kasabov and Q. Song, “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144–154, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. Q. Liu, A. H. Sung, Z. Chen, and J. Xu, “Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images,” Pattern Recognition, vol. 41, no. 1, pp. 56–66, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. E. K. Tang, P. N. Suganthan, and X. Yao, “Gene selection algorithms for microarray data based on least squares support vector machine,” BMC Bioinformatics, vol. 7, article 95, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. N. J. Pizzi, “Classification of biomedical spectra using stochastic feature selection,” Neural Network World, vol. 15, no. 3, pp. 257–268, 2005. View at Google Scholar · View at Scopus
  24. W. Pedrycz, A. Breuer, and N. J. Pizzi, “Fuzzy adaptive logic networks as hybrid models of quantitative software engineering,” Intelligent Automation and Soft Computing, vol. 12, no. 2, pp. 189–209, 2006. View at Google Scholar · View at Scopus
  25. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.
  26. R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, John Wiley & Sons, Hoboken, NJ, USA, 2004.
  27. C. Jacob, Illustrating Evolutionary Computation with Mathematica, Academic Press, San Diego, Calif, USA, 2001.
  28. R. M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, Wiley-IEEE Press, New York, NY, USA, 2001.
  29. R. L. Somorjai, M. E. Alexander, R. Baumgartner et al., “A data-driven, flexible machine learning strategy for the classification of biomedical data,” in Artificial Intelligence Methods and Tools for Systems Biology, W. Dubitzky and F. Azuaje, Eds., pp. 67–85, Springer, Dordrecht, The Netherlands, 2004. View at Google Scholar
  30. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd, Springer, New York, NY, USA, 2009.
  31. B. C. Wheeler and W. J. Heetderks, “A comparison of techniques for classification of multiple neural signals,” IEEE Transactions on Biomedical Engineering, vol. 29, no. 12, pp. 752–759, 1982. View at Google Scholar · View at Scopus